Important: This model uses the JANG quantization format — the GGUF equivalent for MLX on Apple Silicon. Currently only supported by MLX Studio and the
jang-toolsPython package.
Nemotron 3 Super 120B — JANG_4M + CRACK
JANG mixed-precision · CRACK abliterated · Mamba + MoE + Attention · No guardrails · 63 GB
What Is This?
This is NVIDIA Nemotron 3 Super 120B — a 120B parameter hybrid model with THREE layer types: Mamba SSM + MoE (512 experts, top-22) + Attention.
It has been:
- JANG quantized — JANG_4M profile (8-bit attention, 4-bit experts) — 63 GB
- CRACK abliterated — permanent weight-level removal of safety refusal
| Architecture | Nemotron 3 Super — 120B total, ~12B active, 3 layer types |
| Quantization | JANG_4M (8/4-bit mixed, 4.1 avg) — 63 GB |
| HarmBench | 90.3% (289/320) |
| MMLU | 94.2% (196/208 with thinking) |
| Speed | ~40 tok/s (M3 Ultra 256GB) |
| Thinking | ON/OFF supported (ChatML) |
| Fits on | 96 GB+ Macs |
Also see: Nemotron JANG_2L CRACK — 43 GB, 96.2% HarmBench, 95.7% MMLU
HarmBench Results
289/320 (90.3%)
| Category | Score | |
|---|---|---|
| Misinformation / Disinfo | 54/54 | 100% |
| Copyright | 74/80 | 92% |
| Chemical / Biological | 38/42 | 90% |
| Harassment / Bullying | 19/21 | 90% |
| Harmful | 16/18 | 89% |
| Illegal | 46/53 | 87% |
| Cybercrime / Intrusion | 42/52 | 81% |
MMLU Results
196/208 (94.2%) — 208 questions across 13 subjects with thinking recovery
| Subject | Score | /16 | Type |
|---|---|---|---|
| Professional Medicine | 16/16 | 100% | HARD |
| HS Biology | 15/16 | 94% | BASE |
| College Physics | 15/16 | 94% | HARD |
| Conceptual Physics | 15/16 | 94% | HARD |
| Machine Learning | 13/16 | 81% | HARD |
| Electrical Engineering | 13/16 | 81% | HARD |
| College CS | 13/16 | 81% | HARD |
| HS Geography | 14/16 | 88% | BASE |
| World Religions | 14/16 | 88% | BASE |
| Formal Logic | 12/16 | 75% | HARD |
| College Math | 11/16 | 69% | HARD |
| HS Mathematics | 11/16 | 69% | HARD |
| Abstract Algebra | 10/16 | 63% | HARD |
CRACK vs Base
| CRACK | Base JANG_4M | |
|---|---|---|
| MMLU | 94.2% | ~86% |
| HarmBench | 90.3% | 0% |
Install & Usage
pip install "jang[mlx]"
from jang_tools.loader import load_jang_model
from mlx_lm import generate
model, tokenizer = load_jang_model("dealignai/Nemotron-3-Super-120B-A12B-JANG_4M-CRACK")
messages = [{"role": "user", "content": "Your prompt here"}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, tokenize=False)
response = generate(model, tokenizer, prompt=prompt, max_tokens=2000)
print(response)
Thinking Mode
Thinking is ON by default. To disable:
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True,
enable_thinking=False, tokenize=False)
About JANG
JANG (Jang Adaptive N-bit Grading) is a mixed-precision quantization format for Apple Silicon — the GGUF equivalent for MLX.
About CRACK
CRACK (Controlled Refusal Ablation via Calibrated Knockouts) removes safety alignment from LLMs at the weight level.
Links
Disclaimer
This model is provided for research and educational purposes. The creators are not responsible for any misuse.
한국어
Nemotron 3 Super 120B — JANG_4M + CRACK
| 항목 | 내용 |
|---|---|
| 크기 | 63 GB |
| HarmBench | 90.3% (289/320) |
| MMLU | 94.2% (196/208) |
| 속도 | ~40 tok/s (M3 Ultra) |
| 최소 요구사양 | 96 GB 메모리 Mac |
pip install "jang[mlx]"
GitHub · HuggingFace · MLX Studio · Ko-fi · X @dealignai